A Hunger for Data

In today’s digital age, we’re generating more data than ever before.

Technology like our plant phenotyper captures thousands of images each day of the 1,000+ plants that can live in its growth chamber, while our X-ray tomography generates high-quality 3D reconstructions of the internal and external shapes of plants. Capturing images of plants over time helps our scientists understand the variables they’re measuring, whether it is how a genetic change affects plant growth and development, or how plants’ response to a specific stress like heat.

In order to efficiently and effectively analyze these mass amounts of data, our plant scientists collaborate with mathematicians (sometimes referred to as “math-magicians” by their colleagues) like Mao. “I am always hungry for more data,” says Mao with a smile.

“My contribution to our mission is unique from most other scientists at the Danforth Center,” she explains, “but it makes me excited to work in a place where collaboration is essential to success.”

Precision, Prediction, Prescription

Instead of looking at thousands and thousands of images by eye or manually measuring plant features by hand, Mao uses mathematical and computational tools to process images, extract features, and analyze the data. By applying cutting-edge mathematics to plant science data, Mao is helping our plant scientists create more efficient and precise data analysis. Together, they are building models that can help predict plant responses and fully utilize new technologies.

Innovative Approaches to Data

Mao works with two-dimensional (2D) and three-dimensional (3D) data, and was one of the first people to apply persistent homology-based mathematical approaches to plant science. These new approaches help scientists capture large amounts of information about leaf shape or inflorescence topology that previously went undetected, ultimately increasing our understanding of plant forms.

By combining plant science with mathematics, our scientists are increasing our understanding of how to improve plants to produce more food, survive in new environments, resist pests and pathogens, use water more efficiently, and more.

Infinite Possibilities

As one of the few mathematics principal investigators at the Center, Mao is excited about the opportunity to create solutions. “I love what I do because when mathematicians meet data, it means something new will be developing!” As we continue to face challenges for the environment, agriculture, and food security, mathematicians like Mao will be essential to innovating new solutions.

Mao's favorite hobbies

"I'm a fan of Japanese manga called Skip Beat! I also enjoy watching all kinds of tv shows."

Why she loves her job

"I am grateful to have the opportunity to use my skills to help contribute to making the world a better place."

Mao's favorite hobbies

"I'm a fan of Japanese manga called Skip Beat! I also enjoy watching all kinds of tv shows."

Why she loves her job

"I am grateful to have the opportunity to use my skills to help contribute to making the world a better place."

Get in touch with Mao Li

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Research Summary

The Li laboratory develops mathematical methods, models, and computational tools to extract and analyze comprehensive plant morphological features from 2D and 3D imaging data to fully utilize new technologies and accelerate biological discoveries.

Plant Science Meets Mathematicians

As a mathematician, I support the Danforth Center mission to improve the human condition through plant science, but my contribution is unique from most other scientists at the center. Although from different disciplines, we mathematicians, plant biologists, computational scientists, and statisticians work together to achieve a common goal, and try to address problems in plant science such as environmental sustainability, pest and disease resistance, enhancing drought tolerance, and increasing yield in a rapidly growing population. These are big problems and require the interaction of researchers, new technology, and data analysts. When plant science meet mathematicians, it means collaboration!

Data Meet Mathematicians

In recent decades, imaging technologies have developed incredibly quickly. These remarkable advances allow us to collect vast amounts of high context data every day, awaiting measurement and analysis. But can we simply use our eyes to look at countless pictures and accurately judge, for example, which plants are bigger or smaller, or more importantly how different they are? We might use a ruler to manually measure features from the images, such as the length of every branch. However, you can imagine how labor intensive this is! In addition, there are many plant structures such as sorghum panicles that are so complex that most of the features are not tractable with manual measurements. Therefore, we need to employ mathematics and computational tools to process images, extract features, and analyze data. We need to do it efficiently and precisely, to build models to predict the future, and to fully utilize new technologies. This will accelerate discoveries and our understanding of how to modify the plant form to improve agriculture. When data meet mathematicians, it means precision, prediction, and prescription!

Mathematicians meet Data

Data are collected at different scales. For example, they can be microscopic images showing cells, or can be field pictures taken by drones. Data are also collected from different organs – for instance, they can be 2D scans of leaves or can be 3D images of roots. Finally, data can be collected dynamically, such as with a series of images showing plant growth or cell expansion. Data always puts a glint in my mathematician’s eyes, because we see them differently and comprehensively. When mathematicians meet data, it means something new will be developing!

Browse Principal Investigators

Mao Li,